ZOOTask_EDA_modeling_11.07.23
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# Load required packages
# install.packages("dplyr")
# install.packages("Hmisc")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(ggplot2, quietly = TRUE, warn.conflicts = FALSE)
library(emmeans, quietly = TRUE, warn.conflicts = FALSE)
library(lsmeans, quietly = TRUE, warn.conflicts = FALSE)## The 'lsmeans' package is now basically a front end for 'emmeans'.
## Users are encouraged to switch the rest of the way.
## See help('transition') for more information, including how to
## convert old 'lsmeans' objects and scripts to work with 'emmeans'.library(effects, quietly = TRUE, warn.conflicts = FALSE)## Use the command
## lattice::trellis.par.set(effectsTheme())
## to customize lattice options for effects plots.
## See ?effectsTheme for details.library(nlme, quietly = TRUE, warn.conflicts = FALSE)
library(lme4, quietly = TRUE, warn.conflicts = FALSE)
library(lmerTest, quietly = TRUE, warn.conflicts = FALSE)
library(sjPlot, quietly = TRUE, warn.conflicts = FALSE)## Registered S3 methods overwritten by 'parameters':
## method from
## as.double.parameters_kurtosis datawizard
## as.double.parameters_skewness datawizard
## as.double.parameters_smoothness datawizard
## as.numeric.parameters_kurtosis datawizard
## as.numeric.parameters_skewness datawizard
## as.numeric.parameters_smoothness datawizard
## print.parameters_distribution datawizard
## print.parameters_kurtosis datawizard
## print.parameters_skewness datawizard
## summary.parameters_kurtosis datawizard
## summary.parameters_skewness datawizardlibrary(stats, quietly = TRUE, warn.conflicts = FALSE)
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)LOAD ZOO DATAFRAME
ZOO <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO.csv')
# Remove the added X variable after reading it back into R:
ZOO <- ZOO %>%
dplyr::select(-X)
# Display the first 6 rows of te final dataset:
head(ZOO)## Subject StimTag Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 1 AE050318 NegGo Neg Go Left -8.2741357 -12.4529149
## 2 AE050318 NegGo Neg Go Midline -8.6567894 -17.6405009
## 3 AE050318 NegGo Neg Go Right -10.9978696 -17.0612959
## 4 AE050318 NegNoGo Neg NoGo Right 3.0738593 -0.6563204
## 5 AE050318 NegNoGo Neg NoGo Midline 0.1368089 -2.7636031
## 6 AE050318 NegNoGo Neg NoGo Left -10.2330683 -11.7855128
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3 PeakAmp_P3
## 1 7.006061 200 -6.982536 368 -17.043367 512 9.862223
## 2 4.369997 212 -6.242593 352 -23.058663 612 6.037603
## 3 -1.012820 208 -8.224361 352 -20.626624 576 0.844127
## 4 0.449258 232 5.512485 400 -3.018455 508 3.116096
## 5 3.345500 252 3.300071 452 -5.257907 600 6.301873
## 6 2.221935 188 -8.637317 408 -12.417423 648 5.847591
## MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve race
## 1 -4.178779 -10.060831 38.1 0 2
## 2 -8.983711 -16.816070 38.1 0 2
## 3 -6.063426 -12.402263 38.1 0 2
## 4 -3.730180 -8.530940 38.1 0 2
## 5 -2.900412 -8.557978 38.1 0 2
## 6 -1.552445 -3.780106 38.1 0 2
## ethnicity calculator_talkergroup_parent tso_calculated
## 1 0 1 1.9
## 2 0 1 1.9
## 3 0 1 1.9
## 4 0 1 1.9
## 5 0 1 1.9
## 6 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy premature_responses RT_proper RT_all_go TrialNum
## 1 75.83333 9.90099 774.044 706.0297 31
## 2 75.83333 9.90099 774.044 706.0297 31
## 3 75.83333 9.90099 774.044 706.0297 31
## 4 57.50000 23.52941 566.000 NA 10
## 5 57.50000 23.52941 566.000 NA 10
## 6 57.50000 23.52941 566.000 NA 10LOAD ZOO_GOOD DATAFRAME
ZOO_good <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO_good.csv')
# Remove the added X variable after reading it back into R:
ZOO_good <- ZOO_good %>%
dplyr::select(-X)
# Display the first 6 rows of te final dataset:
head(ZOO_good)## Subject StimTag Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 1 AE050318 NegGo Neg Go Left -8.2741357 -12.4529149
## 2 AE050318 NegGo Neg Go Midline -8.6567894 -17.6405009
## 3 AE050318 NegGo Neg Go Right -10.9978696 -17.0612959
## 4 AE050318 NegNoGo Neg NoGo Right 3.0738593 -0.6563204
## 5 AE050318 NegNoGo Neg NoGo Midline 0.1368089 -2.7636031
## 6 AE050318 NegNoGo Neg NoGo Left -10.2330683 -11.7855128
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3 PeakAmp_P3
## 1 7.006061 200 -6.982536 368 -17.043367 512 9.862223
## 2 4.369997 212 -6.242593 352 -23.058663 612 6.037603
## 3 -1.012820 208 -8.224361 352 -20.626624 576 0.844127
## 4 0.449258 232 5.512485 400 -3.018455 508 3.116096
## 5 3.345500 252 3.300071 452 -5.257907 600 6.301873
## 6 2.221935 188 -8.637317 408 -12.417423 648 5.847591
## MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve race
## 1 -4.178779 -10.060831 38.1 0 2
## 2 -8.983711 -16.816070 38.1 0 2
## 3 -6.063426 -12.402263 38.1 0 2
## 4 -3.730180 -8.530940 38.1 0 2
## 5 -2.900412 -8.557978 38.1 0 2
## 6 -1.552445 -3.780106 38.1 0 2
## ethnicity calculator_talkergroup_parent tso_calculated
## 1 0 1 1.9
## 2 0 1 1.9
## 3 0 1 1.9
## 4 0 1 1.9
## 5 0 1 1.9
## 6 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy premature_responses RT_proper RT_all_go TrialNum
## 1 75.83333 9.90099 774.044 706.0297 31
## 2 75.83333 9.90099 774.044 706.0297 31
## 3 75.83333 9.90099 774.044 706.0297 31
## 4 57.50000 23.52941 566.000 NA 10
## 5 57.50000 23.52941 566.000 NA 10
## 6 57.50000 23.52941 566.000 NA 10print(names(ZOO_good))## [1] "Subject" "StimTag"
## [3] "Emotion" "Condition"
## [5] "Laterality" "MeanAmp_P2"
## [7] "MeanAmp_N2" "MeanAmp_P3"
## [9] "Latency_P2" "PeakAmp_P2"
## [11] "Latency_N2" "PeakAmp_N2"
## [13] "Latency_P3" "PeakAmp_P3"
## [15] "MeanAmp_N2P2" "PeakAmp_N2P2"
## [17] "calculator_age_cve" "calculator_gender_cve"
## [19] "race" "ethnicity"
## [21] "calculator_talkergroup_parent" "tso_calculated"
## [23] "disfluency_sldper100words" "ssi_total"
## [25] "disfluency_sldper100words_final" "talkergroup_final"
## [27] "gfta_standard" "ppvt_standard"
## [29] "evt_standard" "teld_rec_standard"
## [31] "teld_exp_standard" "teld_spokenlang_standard"
## [33] "tocs_1_total" "tocs_2_total"
## [35] "tcs_total" "eprime_condorder_zootask"
## [37] "cve_comments" "comments_tasks"
## [39] "handedness_zoo" "accuracy"
## [41] "premature_responses" "RT_proper"
## [43] "RT_all_go" "TrialNum"CREATE DATASETS FOR:
- ERP and BEHAVIORAL ANALYSES –> ZOO_FCz_Pz
- AGE-GENDER ANALYSES –> ZOO_age_gender
# DATAFRAME FOR ERP and BEHAVIORAL ANALYSES:
ZOO_FCz_Pz <- subset(ZOO_good, (Laterality == "Midline"))
rownames(ZOO_FCz_Pz) <- NULL # reset index
# DATAFRAME FOR AGE-GENDER ANALYSE ANALYSES:
# Create a dataset with only 1 entry for each subject:
# Pick one condition - randomly - because we are only working on age and gender for this one:
ZOO_age_gender <- subset(ZOO_FCz_Pz, (StimTag == "NegGo"))
rownames(ZOO_age_gender) <- NULL # reset indexDISCRIPTIVE STATISTICS FOR AGE, GENDER
# Give Age, Gender and Count Summary Statistics for each Group:
ZOO_age_gender_summary <-
ZOO_age_gender %>%
group_by(talkergroup_final) %>%
summarise(
age = mean(calculator_age_cve, na.rm = TRUE),
min_age = min(calculator_age_cve, na.rm = TRUE),
max_age = max(calculator_age_cve, na.rm = TRUE),
gender = mean(calculator_gender_cve, na.rm = TRUE),
SD_age = sd(calculator_age_cve, na.rm = TRUE),
Count_kids = n_distinct(Subject)
)
print(ZOO_age_gender_summary)## # A tibble: 2 × 7
## talkergroup_final age min_age max_age gender SD_age Count_kids
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0 57.5 38 81.7 0.548 12.6 42
## 2 1 58.4 36.6 79.4 0.595 13.1 37# Histogram of Age across all participants
hist(ZOO_age_gender$calculator_age_cve,
main = "Histogram of Age",
xlab = "age in months",
ylab = "Frequency",
col = "blue", # Color of bars
border = "black", # Border color of bars
# xlim = c(min_value, max_value), # Adjust the x-axis limits if needed
breaks = 12) 
# print(quantile(ZOO$calculator_age_cve, 0.50, na.rm = TRUE),)
# CREATE TWO PLOTS FOR CWS AND CWNS
# Create a new plot
par(mfrow=c(1,2)) # This sets up a 1x2 grid for side-by-side histograms
# Histogram for talkergroup_final == 1
hist(ZOO_age_gender$calculator_age_cve[ZOO_age_gender$talkergroup_final == 0],
main = "Histogram for CWNS",
xlab = "age in months",
ylab = "Frequency",
col = "blue",
border = "black",
xlim = c(30, 100),
breaks = 12)
# Histogram for talkergroup_final == 2
hist(ZOO_age_gender$calculator_age_cve[ZOO_age_gender$talkergroup_final == 1],
main = "Histogram for CWS",
xlab = "age in months",
ylab = "Frequency",
col = "red", # Use a different color for the second group
border = "black",
xlim = c(30, 100),
breaks = 12)
# Reset the plotting to a single plot
par(mfrow=c(1,1))
# MAKE SURE AGE DOES NOT DIFFER BETWEEN GROUPS USING ANOVA
model <- aov (calculator_age_cve ~ talkergroup_final, data = ZOO_age_gender)
anova(model)## Analysis of Variance Table
##
## Response: calculator_age_cve
## Df Sum Sq Mean Sq F value Pr(>F)
## talkergroup_final 1 16 15.972 0.0969 0.7564
## Residuals 77 12688 164.786# MAKE SURE AGE DOES NOT DIFFER BETWEEN GROUPS USING T-Test
# Subset the data into two groups based on talkergroup_final
group_CWNS <- ZOO_age_gender$calculator_age_cve[ZOO_age_gender$talkergroup_final == 0]
group_CWS <- ZOO_age_gender$calculator_age_cve[ZOO_age_gender$talkergroup_final == 1]
# Perform a two-sample t-test
t_test_result <- t.test(group_CWNS, group_CWS, alternative = "two.sided")
# Display the t-test result
print(t_test_result)##
## Welch Two Sample t-test
##
## data: group_CWNS and group_CWS
## t = -0.31049, df = 74.829, p-value = 0.757
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.682676 4.880488
## sample estimates:
## mean of x mean of y
## 57.48810 58.38919BEHAVIORAL DATA SUMMARY STATISTICS
# Histogram of Accuracy, premature_responses, and RT_proper
summary_behavioral_hist <-
ZOO_FCz_Pz %>%
group_by(Subject, talkergroup_final, StimTag) %>%
summarise(Average_accuracy = mean(accuracy, na.rm = TRUE),
Average_premature_responses = mean(premature_responses, na.rm = TRUE),
Average_RT = mean(RT_proper, na.rm = TRUE))## `summarise()` has grouped output by 'Subject', 'talkergroup_final'. You can
## override using the `.groups` argument.print(summary_behavioral_hist)## # A tibble: 316 × 6
## # Groups: Subject, talkergroup_final [79]
## Subject talkergroup_final StimTag Average_accuracy Average_premature_resp…¹
## <chr> <int> <chr> <dbl> <dbl>
## 1 AE050318 1 NegGo 75.8 9.90
## 2 AE050318 1 NegNoGo 57.5 23.5
## 3 AE050318 1 NeutGo 90 0.917
## 4 AE050318 1 NeutNoGo 70 16.7
## 5 AH101121 0 NegGo 99.2 0
## 6 AH101121 0 NegNoGo 87.5 0
## 7 AH101121 0 NeutGo 100 0
## 8 AH101121 0 NeutNoGo 80 0
## 9 AK022218 0 NegGo 90 0.917
## 10 AK022218 0 NegNoGo 95 0
## # ℹ 306 more rows
## # ℹ abbreviated name: ¹Average_premature_responses
## # ℹ 1 more variable: Average_RT <dbl># Accuracy Histogram:
# Create a new plot
par(mfrow=c(1,2)) # This sets up a 1x2 grid for side-by-side histograms
# Histogram for talkergroup_final == 0
hist(summary_behavioral_hist$Average_accuracy[summary_behavioral_hist$talkergroup_final == 0],
main = "Histogram for CWNS",
xlab = "accuracy",
ylab = "Frequency",
col = "blue",
border = "black",
xlim = c(0, 100),
ylim = c(0, 50),
breaks = 12)
# Histogram for talkergroup_final == 1
hist(summary_behavioral_hist$Average_accuracy[summary_behavioral_hist$talkergroup_final == 1],
main = "Histogram for CWS",
xlab = "accuracy",
ylab = "Frequency",
col = "red", # Use a different color for the second group
border = "black",
xlim = c(0, 100),
ylim = c(0, 50),
breaks = 12)
# Reset the plotting to a single plot
par(mfrow=c(1,1))
# Scatterplot of all accuracy across all participants
scatterplot <- ggplot(summary_behavioral_hist, aes(x = Subject, y = Average_accuracy, color = StimTag)) +
geom_point() +
scale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, by = 10))
plot(scatterplot)
# Premature responses Histogram:
# Create a new plot
par(mfrow=c(1,2))
# Histogram for talkergroup_final == 0
hist(summary_behavioral_hist$Average_premature_responses[summary_behavioral_hist$talkergroup_final == 0],
main = "Histogram for CWNS",
xlab = "premature_responses",
ylab = "Frequency",
col = "blue",
border = "black",
xlim = c(0, 50),
ylim = c(0, 150),
breaks = 12)
# Histogram for talkergroup_final == 1
hist(summary_behavioral_hist$Average_premature_responses[summary_behavioral_hist$talkergroup_final == 1],
main = "Histogram for CWS",
xlab = "premature_responses",
ylab = "Frequency",
col = "red", # Use a different color for the second group
border = "black",
xlim = c(0, 50),
ylim = c(0, 150),
breaks = 12)
# Reset the plotting to a single plot
par(mfrow=c(1,1))
# Scatterplot of all accuracy across all participants
scatterplot <- ggplot(summary_behavioral_hist, aes(x = Subject, y = Average_premature_responses, color = StimTag)) +
geom_point() +
scale_y_continuous(limits = c(0, 50), breaks = seq(0, 50, by = 10))
plot(scatterplot)## Warning: Removed 5 rows containing missing values (`geom_point()`).
#Reaction Time Histogram:
# Create a new plot
par(mfrow=c(1,2))
# Histogram for talkergroup_final == 0
hist(summary_behavioral_hist$Average_RT[summary_behavioral_hist$talkergroup_final == 0],
main = "Histogram for CWNS",
xlab = "reaction_time",
ylab = "Frequency",
col = "blue",
border = "black",
xlim = c(100, 2000),
ylim = c(0, 50),
breaks = 12)
# Histogram for talkergroup_final == 1
hist(summary_behavioral_hist$Average_RT[summary_behavioral_hist$talkergroup_final == 1],
main = "Histogram for CWS",
xlab = "reaction_time",
ylab = "Frequency",
col = "red", # Use a different color for the second group
border = "black",
xlim = c(100, 2000),
ylim = c(0, 50),
breaks = 12)
# Reset the plotting to a single plot
par(mfrow=c(1,1))
# Scatterplot of all reaction time across all participants
scatterplot <- ggplot(summary_behavioral_hist, aes(x = Subject, y = Average_RT, color = StimTag)) +
geom_point() +
scale_y_continuous(limits = c(200, 2000), breaks = seq(200, 2000, by = 200))
plot(scatterplot)## Warning: Removed 5 rows containing missing values (`geom_point()`).
BARPLOTS FOR BEHAVIORAL DATA:
# Create the Dataframe to get the behavioral data
summary_behavioral <-
ZOO_FCz_Pz%>%
group_by(talkergroup_final, Emotion, Condition) %>%
summarise(
Average_accuracy = mean(accuracy, na.rm = TRUE),
SEM_accuracy = sd(accuracy, na.rm = TRUE) / sqrt(n()),
Average_premature_responses = mean(premature_responses, na.rm = TRUE),
SEM_premature_responses = sd(premature_responses, na.rm = TRUE) / sqrt(n()),
Average_RT_proper = mean(RT_proper, na.rm = TRUE),
SEM_RT_proper = sd(RT_proper, na.rm = TRUE) / sqrt(n()),
)## `summarise()` has grouped output by 'talkergroup_final', 'Emotion'. You can
## override using the `.groups` argument.# Rename some factors for better visuals:
summary_behavioral$talkergroup_final <- factor(summary_behavioral$talkergroup_final, levels = c(0, 1), labels = c("CWNS", "CWS"))
summary_behavioral$Emotion <- factor(summary_behavioral$Emotion, levels = c("Neg", "Neut"), labels = c("Affective", "Neutral"))
# BAR PLOT FOR ACCURACY
bar_plot <- ggplot(summary_behavioral, aes(x = Emotion, y = Average_accuracy, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(aes(ymin = Average_accuracy - SEM_accuracy, ymax = Average_accuracy + SEM_accuracy), width = 0.2, position = position_dodge(width = 0.7)) +
labs(x = "Emotion", y = "Average Accuracy (%)") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered"))
# ggtitle("Average Accuracy by Emotion and Condition for CWS and CWNS")
bar_plot <- bar_plot + coord_cartesian(ylim = c(50, 100))
print(bar_plot)
# BAR PLOT FOR PREMATURE RESPONSES
bar_plot <- ggplot(summary_behavioral, aes(x = Emotion, y = Average_premature_responses, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(aes(ymin = Average_premature_responses - SEM_premature_responses, ymax = Average_premature_responses + SEM_premature_responses), width = 0.2, position = position_dodge(width = 0.7)) +
labs(x = "Emotion", y = "Average_premature_responses (%)") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered"))
# ggtitle("Average Accuracy by Emotion and Condition for CWS and CWNS")
bar_plot <- bar_plot + coord_cartesian(ylim = c(0, 10))
print(bar_plot)
# BAR PLOT FOR REACTION TIME - both go and nogo
bar_plot <- ggplot(summary_behavioral, aes(x = Emotion, y = Average_RT_proper, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(aes(ymin = Average_RT_proper - SEM_RT_proper, ymax = Average_RT_proper + SEM_RT_proper), width = 0.2, position = position_dodge(width = 0.7)) +
labs(x = "Emotion", y = "Average Reaction Time (ms)") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered"))
# ggtitle("Average Accuracy by Emotion and Condition for CWS and CWNS")
bar_plot <- bar_plot + coord_cartesian(ylim = c(500, 850))
print(bar_plot)
# BAR PLOT FOR REACTION TIME - GO ONLY
summary_behavioral_Go <- subset(summary_behavioral, Condition == "Go")
bar_plot <- ggplot(summary_behavioral_Go, aes(x = Emotion, y = Average_RT_proper)) +
geom_bar(stat = "identity", position = "dodge", width = 0.5, fill ='royalblue2') +
geom_errorbar(aes(ymin = Average_RT_proper - SEM_RT_proper, ymax = Average_RT_proper + SEM_RT_proper), width = 0.2, position = position_dodge(width = 0.9)) +
labs(x = "Emotion", y = "Average Reaction Time (ms)") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold"))
# ggtitle("Average Accuracy by Emotion and Condition for CWS and CWNS")
bar_plot <- bar_plot + coord_cartesian(ylim = c(500, 850))
print(bar_plot)
BEHAVIORAL ANALYSES - ACCURACY:
# ACCURACY
model1 <- lmer(accuracy ~ talkergroup_final*Emotion*Condition +
calculator_gender_cve + calculator_age_cve + (1|Subject), data = ZOO_FCz_Pz, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 268.0 268.0 1 75 2.8230
## Emotion 43.4 43.4 1 231 0.4573
## Condition 10044.5 10044.5 1 231 105.8123
## calculator_gender_cve 75.3 75.3 1 75 0.7931
## calculator_age_cve 604.9 604.9 1 75 6.3723
## talkergroup_final:Emotion 5.4 5.4 1 231 0.0567
## talkergroup_final:Condition 109.8 109.8 1 231 1.1570
## Emotion:Condition 43.7 43.7 1 231 0.4600
## talkergroup_final:Emotion:Condition 84.3 84.3 1 231 0.8879
## Pr(>F)
## talkergroup_final 0.09708 .
## Emotion 0.49955
## Condition < 2e-16 ***
## calculator_gender_cve 0.37601
## calculator_age_cve 0.01371 *
## talkergroup_final:Emotion 0.81202
## talkergroup_final:Condition 0.28320
## Emotion:Condition 0.49829
## talkergroup_final:Emotion:Condition 0.34703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
emmeans_result <- emmeans(model1, pairwise ~ talkergroup_final | Emotion | Condition) # Tukey is default
pairs(emmeans_result, adjust= "none", simple = "each") # adjust = "bonferroni"## $emmeans
## $`simple contrasts for talkergroup_final`
## Emotion = Neg, Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.57 2.6 242 0.990 0.3234
##
## Emotion = Neut, Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 1.03 2.6 242 0.395 0.6932
##
## Emotion = Neg, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.87 2.6 242 1.102 0.2715
##
## Emotion = Neut, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 5.46 2.6 242 2.099 0.0369
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 2.03636 2.13 231 0.958 0.3392
##
## talkergroup_final = 1, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 0.48940 2.27 231 0.216 0.8291
##
## talkergroup_final = 0, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.00298 2.13 231 -0.001 0.9989
##
## talkergroup_final = 1, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 2.59005 2.27 231 1.143 0.2541
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 16.5 2.13 231 7.753 <.0001
##
## talkergroup_final = 1, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 16.8 2.27 231 7.406 <.0001
##
## talkergroup_final = 0, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 14.4 2.13 231 6.794 <.0001
##
## talkergroup_final = 1, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 18.9 2.27 231 8.334 <.0001
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Emotion = Neg, Condition = Go:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut, Condition = Go:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neg, Condition = NoGo:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut, Condition = NoGo:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1, Condition = Go:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 1.55 3.11 231 0.498 0.6190
##
## contrast = 0 - 1, Condition = NoGo:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut -2.59 3.11 231 -0.835 0.4048
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1, Emotion = Neg:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -0.293 3.11 231 -0.094 0.9250
##
## contrast = 0 - 1, Emotion = Neut:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -4.433 3.11 231 -1.427 0.1550
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resultCondition <- emmeans(model1, pairwise ~ Condition) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resultCondition## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Go 92.0 1.05 136 89.9 94.1
## NoGo 75.3 1.05 136 73.3 77.4
##
## Results are averaged over the levels of: talkergroup_final, Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Go - NoGo 16.6 1.1 231 15.155 <.0001
##
## Results are averaged over the levels of: talkergroup_final, Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupEmotion <- emmeans(model1, pairwise ~ talkergroup_final|Emotion) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroupEmotion## $emmeans
## Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 85.7 1.43 137 82.8 88.5
## 1 82.9 1.53 136 79.9 86.0
##
## Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 84.6 1.43 137 81.8 87.5
## 1 81.4 1.53 136 78.4 84.4
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.72 2.09 137 1.304 0.1946
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 3.24 2.09 137 1.554 0.1225
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupEmotion, adjust = "none", simple = "each") ## $emmeans
## $`simple contrasts for talkergroup_final`
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.72 2.09 137 1.304 0.1946
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 3.24 2.09 137 1.554 0.1225
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 1.02 1.5 231 0.676 0.4995
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 1.54 1.6 231 0.961 0.3374
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut -0.523 2.2 231 -0.238 0.8120
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroup <- emmeans(model1, pairwise ~ talkergroup_final) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroup## $emmeans
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 85.2 1.22 75 82.7 87.6
## 1 82.2 1.30 75 79.6 84.8
##
## Results are averaged over the levels of: Emotion, Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.98 1.78 75 1.680 0.0971
##
## Results are averaged over the levels of: Emotion, Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupCond <- emmeans(model1, pairwise ~ talkergroup_final|Condition) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroupCond## $emmeans
## Condition = Go:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 92.9 1.43 137 90.1 95.7
## 1 91.1 1.53 136 88.1 94.1
##
## Condition = NoGo:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 77.4 1.43 137 74.6 80.3
## 1 73.3 1.53 136 70.2 76.3
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 1.80 2.09 137 0.863 0.3897
##
## Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 4.16 2.09 137 1.995 0.0481
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupCond, adjust = "none", simple = "each") ## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 1.80 2.09 137 0.863 0.3897
##
## Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 4.16 2.09 137 1.995 0.0481
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 15.5 1.5 231 10.287 <.0001
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 17.8 1.6 231 11.130 <.0001
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -2.36 2.2 231 -1.076 0.2832
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupCondEmo <- emmeans(model1, pairwise ~ talkergroup_final|Condition|Emotion)
emmeans_resulttalkergroupCondEmo## $emmeans
## Condition = Go, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 93.9 1.78 242 90.4 97.4
## 1 91.3 1.90 241 87.6 95.1
##
## Condition = NoGo, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 77.4 1.78 242 73.9 80.9
## 1 74.6 1.90 241 70.8 78.3
##
## Condition = Go, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 91.9 1.78 242 88.4 95.4
## 1 90.8 1.90 241 87.1 94.6
##
## Condition = NoGo, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 77.4 1.78 242 73.9 80.9
## 1 72.0 1.90 241 68.2 75.7
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.57 2.6 242 0.990 0.3234
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.87 2.6 242 1.102 0.2715
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 1.03 2.6 242 0.395 0.6932
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 5.46 2.6 242 2.099 0.0369
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupCondEmo, adjust = "none", simple = "each")## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.57 2.6 242 0.990 0.3234
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 2.87 2.6 242 1.102 0.2715
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 1.03 2.6 242 0.395 0.6932
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 5.46 2.6 242 2.099 0.0369
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 16.5 2.13 231 7.753 <.0001
##
## talkergroup_final = 1, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 16.8 2.27 231 7.406 <.0001
##
## talkergroup_final = 0, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 14.4 2.13 231 6.794 <.0001
##
## talkergroup_final = 1, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 18.9 2.27 231 8.334 <.0001
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 2.03636 2.13 231 0.958 0.3392
##
## talkergroup_final = 1, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 0.48940 2.27 231 0.216 0.8291
##
## talkergroup_final = 0, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.00298 2.13 231 -0.001 0.9989
##
## talkergroup_final = 1, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 2.59005 2.27 231 1.143 0.2541
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1, Emotion = Neg:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -0.293 3.11 231 -0.094 0.9250
##
## contrast = 0 - 1, Emotion = Neut:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -4.433 3.11 231 -1.427 0.1550
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1, Condition = Go:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 1.55 3.11 231 0.498 0.6190
##
## contrast = 0 - 1, Condition = NoGo:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut -2.59 3.11 231 -0.835 0.4048
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerBEHAVIORAL ANALYSES - PREMATURE RESPONSES:
# PREMATURE RESPONSES
model1 <- lmer(premature_responses ~ talkergroup_final*Emotion*Condition +
calculator_gender_cve + calculator_age_cve + (1|Subject), data = ZOO_FCz_Pz, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 93.162 93.162 1 74.107 4.2783
## Emotion 69.043 69.043 1 227.126 3.1707
## Condition 151.067 151.067 1 227.126 6.9375
## calculator_gender_cve 2.866 2.866 1 74.028 0.1316
## calculator_age_cve 236.199 236.199 1 73.909 10.8471
## talkergroup_final:Emotion 74.558 74.558 1 227.378 3.4239
## talkergroup_final:Condition 101.711 101.711 1 227.378 4.6709
## Emotion:Condition 69.597 69.597 1 227.126 3.1962
## talkergroup_final:Emotion:Condition 52.435 52.435 1 227.378 2.4080
## Pr(>F)
## talkergroup_final 0.042091 *
## Emotion 0.076306 .
## Condition 0.009021 **
## calculator_gender_cve 0.717773
## calculator_age_cve 0.001520 **
## talkergroup_final:Emotion 0.065555 .
## talkergroup_final:Condition 0.031722 *
## Emotion:Condition 0.075145 .
## talkergroup_final:Emotion:Condition 0.122108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
emmeans_result <- emmeans(model1, pairwise ~ talkergroup_final | Condition) # Tukey is default## NOTE: Results may be misleading due to involvement in interactionspairs(emmeans_result, adjust= "none", simple = "each") # adjust = "bonferroni"## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.532 0.969 142 -0.549 0.5840
##
## Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -2.828 0.972 143 -2.910 0.0042
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -1.90 0.720 227 -2.634 0.0090
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -4.19 0.781 227 -5.368 <.0001
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo 2.3 1.06 227 2.161 0.0317
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resultCondition <- emmeans(model1, pairwise ~ Condition) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resultCondition## $emmeans
## Condition emmean SE df lower.CL upper.CL
## Go 1.54 0.487 141 0.581 2.51
## NoGo 4.59 0.488 142 3.623 5.55
##
## Results are averaged over the levels of: talkergroup_final, Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Go - NoGo -3.04 0.531 227 -5.732 <.0001
##
## Results are averaged over the levels of: talkergroup_final, Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupEmotion <- emmeans(model1, pairwise ~ talkergroup_final|Emotion) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroupEmotion## $emmeans
## Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 2.87 0.659 142 1.564 4.17
## 1 3.56 0.714 141 2.153 4.98
##
## Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 1.58 0.659 142 0.282 2.89
## 1 4.25 0.717 143 2.830 5.66
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.697 0.969 142 -0.719 0.4733
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -2.662 0.972 143 -2.740 0.0069
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupEmotion, adjust = "none", simple = "each") ## $emmeans
## $`simple contrasts for talkergroup_final`
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.697 0.969 142 -0.719 0.4733
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -2.662 0.972 143 -2.740 0.0069
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 1.282 0.720 227 1.781 0.0763
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.683 0.781 227 -0.875 0.3825
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 1.97 1.06 227 1.850 0.0656
##
## Results are averaged over the levels of: Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroup <- emmeans(model1, pairwise ~ talkergroup_final) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroup## $emmeans
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 2.23 0.552 73.8 1.13 3.33
## 1 3.91 0.600 74.2 2.71 5.10
##
## Results are averaged over the levels of: Emotion, Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.68 0.812 74 -2.068 0.0421
##
## Results are averaged over the levels of: Emotion, Condition, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupCond <- emmeans(model1, pairwise ~ talkergroup_final|Condition) ## NOTE: Results may be misleading due to involvement in interactionsemmeans_resulttalkergroupCond## $emmeans
## Condition = Go:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 1.28 0.659 142 -0.0251 2.58
## 1 1.81 0.714 141 0.3982 3.22
##
## Condition = NoGo:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 3.17 0.659 142 1.8714 4.48
## 1 6.00 0.717 143 4.5841 7.42
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.532 0.969 142 -0.549 0.5840
##
## Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -2.828 0.972 143 -2.910 0.0042
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupCond, adjust = "none", simple = "each") ## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.532 0.969 142 -0.549 0.5840
##
## Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -2.828 0.972 143 -2.910 0.0042
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -1.90 0.720 227 -2.634 0.0090
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -4.19 0.781 227 -5.368 <.0001
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo 2.3 1.06 227 2.161 0.0317
##
## Results are averaged over the levels of: Emotion, calculator_gender_cve
## Degrees-of-freedom method: kenward-rogeremmeans_resulttalkergroupCondEmo <- emmeans(model1, pairwise ~ talkergroup_final|Condition|Emotion)
emmeans_resulttalkergroupCondEmo## $emmeans
## Condition = Go, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 1.28 0.833 250 -0.365 2.92
## 1 1.65 0.901 249 -0.127 3.42
##
## Condition = NoGo, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 4.46 0.833 250 2.819 6.10
## 1 5.48 0.901 249 3.705 7.25
##
## Condition = Go, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 1.28 0.833 250 -0.360 2.92
## 1 1.97 0.901 249 0.196 3.75
##
## Condition = NoGo, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 1.89 0.833 250 0.249 3.53
## 1 6.52 0.912 252 4.728 8.32
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.373 1.23 250 -0.305 0.7609
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.021 1.23 250 -0.833 0.4056
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.690 1.23 250 -0.564 0.5736
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -4.635 1.23 252 -3.758 0.0002
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupCondEmo, adjust = "none", simple = "each") ## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.373 1.23 250 -0.305 0.7609
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.021 1.23 250 -0.833 0.4056
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.690 1.23 250 -0.564 0.5736
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -4.635 1.23 252 -3.758 0.0002
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -3.184 1.02 227 -3.127 0.0020
##
## talkergroup_final = 1, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -3.831 1.10 227 -3.483 0.0006
##
## talkergroup_final = 0, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -0.609 1.02 227 -0.598 0.5502
##
## talkergroup_final = 1, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo -4.553 1.11 228 -4.106 0.0001
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.00513 1.02 227 -0.005 0.9960
##
## talkergroup_final = 1, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.32234 1.10 227 -0.293 0.7697
##
## talkergroup_final = 0, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 2.56942 1.02 227 2.523 0.0123
##
## talkergroup_final = 1, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -1.04453 1.11 228 -0.942 0.3473
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1, Emotion = Neg:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo 0.647 1.50 227 0.432 0.6662
##
## contrast = 0 - 1, Emotion = Neut:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo 3.944 1.51 228 2.620 0.0094
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1, Condition = Go:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 0.317 1.50 227 0.212 0.8326
##
## contrast = 0 - 1, Condition = NoGo:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 3.614 1.51 228 2.400 0.0172
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerBEHAVIORAL ANALYSES - REACTION TIME:
ZOO_FCz_Pz_GO <- subset(ZOO_FCz_Pz, Condition == 'Go')
ZOO_FCz_Pz_NOGO <- subset(ZOO_FCz_Pz, Condition == 'NoGo')
model1 <- lmer(RT_proper ~ talkergroup_final*Emotion +
calculator_gender_cve + calculator_age_cve + (1|Subject), data = ZOO_FCz_Pz_GO, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## talkergroup_final 8810 8810 1 74 2.8719 0.09434 .
## Emotion 5689 5689 1 76 1.8545 0.17728
## calculator_gender_cve 5399 5399 1 74 1.7601 0.18869
## calculator_age_cve 161404 161404 1 74 52.6144 3.3e-10 ***
## talkergroup_final:Emotion 160 160 1 76 0.0521 0.81998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "talkergroup_final")) 
emmeans_result <- emmeans(model1, pairwise ~ talkergroup_final | Emotion) # Tukey is default
pairs(emmeans_result, adjust= "none", simple = "each") # adjust = "bonferroni"## $emmeans
## $`simple contrasts for talkergroup_final`
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -37.1 22.5 100 -1.647 0.1026
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -33.1 22.5 100 -1.467 0.1455
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 16.5 12.1 76 1.362 0.1773
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 20.5 13.1 76 1.572 0.1201
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut -4.06 17.8 76 -0.228 0.8200
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogermodel1 <- lmer(RT_proper ~ talkergroup_final*Emotion +
calculator_gender_cve + calculator_age_cve + (1|Subject), data = ZOO_FCz_Pz_NOGO, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## talkergroup_final 115131 115131 1 73.832 4.8966 0.0300 *
## Emotion 23068 23068 1 75.014 0.9811 0.3251
## calculator_gender_cve 34253 34253 1 73.675 1.4568 0.2313
## calculator_age_cve 910160 910160 1 73.436 38.7098 2.733e-08 ***
## talkergroup_final:Emotion 1892 1892 1 75.513 0.0805 0.7774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "talkergroup_final")) 
emmeans_result <- emmeans(model1, pairwise ~ talkergroup_final | Emotion) # Tukey is default
pairs(emmeans_result, adjust= "none", simple = "each") # adjust = "bonferroni"## $emmeans
## $`simple contrasts for talkergroup_final`
## Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -72.6 43.5 131 -1.667 0.0978
##
## Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -86.6 43.8 132 -1.977 0.0501
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -33.1 33.5 75.1 -0.991 0.3251
##
## talkergroup_final = 1:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -47.2 36.5 76.0 -1.293 0.2000
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 14 49.5 75.6 0.284 0.7775
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogermodel1 <- aov(RT_proper ~ talkergroup_final*Emotion + calculator_age_cve +calculator_gender_cve + Error(Subject/(Emotion)), data = ZOO_FCz_Pz_GO)
summary(model1)##
## Error: Subject
## Df Sum Sq Mean Sq F value Pr(>F)
## talkergroup_final 1 30478 30478 1.836 0.179
## calculator_age_cve 1 877460 877460 52.872 3.06e-10 ***
## calculator_gender_cve 1 29211 29211 1.760 0.189
## Residuals 74 1228107 16596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Subject:Emotion
## Df Sum Sq Mean Sq F value Pr(>F)
## Emotion 1 13110 13110 4.274 0.0421 *
## talkergroup_final:Emotion 1 160 160 0.052 0.8200
## Residuals 76 233143 3068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1N2 ERP component is a negative-going event-related potential that is typically observed in a time window approximately 200-350 ms after the onset of a stimulus. It is generally linked with conflict monitoring, error detection, and response inhibition processes in cognitive control tasks. It is also often seen in tasks that require the detection of rare or unexpected events, hence it is sometimes referred to as the “mismatch negativity” or “surprise” signal.
# Without random subject
model1 <- aov(MeanAmp_N2P2 ~ (talkergroup_final*Emotion*Condition) + calculator_gender_cve + calculator_age_cve + Error(Subject/(Emotion*Condition)), data = ZOO_FCz_Pz)
summary(model1)##
## Error: Subject
## Df Sum Sq Mean Sq F value Pr(>F)
## talkergroup_final 1 30 30.18 0.662 0.418
## calculator_gender_cve 1 3 3.08 0.068 0.796
## calculator_age_cve 1 4 4.31 0.095 0.759
## Residuals 75 3418 45.57
##
## Error: Subject:Emotion
## Df Sum Sq Mean Sq F value Pr(>F)
## Emotion 1 19.9 19.881 2.962 0.0893 .
## talkergroup_final:Emotion 1 0.1 0.111 0.017 0.8978
## Residuals 77 516.8 6.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Subject:Condition
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 1 341.1 341.1 38.524 2.53e-08 ***
## talkergroup_final:Condition 1 1.0 1.0 0.112 0.739
## Residuals 77 681.8 8.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Subject:Emotion:Condition
## Df Sum Sq Mean Sq F value Pr(>F)
## Emotion:Condition 1 46.8 46.79 8.100 0.00567 **
## talkergroup_final:Emotion:Condition 1 30.4 30.45 5.272 0.02440 *
## Residuals 77 444.8 5.78
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# LATERALITY INCLUDED (MAIN EFFECT OF EMOTION)
model1 <- lmer(MeanAmp_N2P2 ~ talkergroup_final*Emotion*Condition*Laterality + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_good, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF
## talkergroup_final 3.70 3.70 1 75
## Emotion 51.22 51.22 1 847
## Condition 404.12 404.12 1 847
## Laterality 101.52 50.76 2 847
## calculator_gender_cve 0.90 0.90 1 75
## calculator_age_cve 6.14 6.14 1 75
## talkergroup_final:Emotion 3.42 3.42 1 847
## talkergroup_final:Condition 7.39 7.39 1 847
## Emotion:Condition 4.35 4.35 1 847
## talkergroup_final:Laterality 32.58 16.29 2 847
## Emotion:Laterality 7.77 3.89 2 847
## Condition:Laterality 10.43 5.22 2 847
## talkergroup_final:Emotion:Condition 62.17 62.17 1 847
## talkergroup_final:Emotion:Laterality 1.83 0.91 2 847
## talkergroup_final:Condition:Laterality 11.62 5.81 2 847
## Emotion:Condition:Laterality 3.03 1.52 2 847
## talkergroup_final:Emotion:Condition:Laterality 1.43 0.72 2 847
## F value Pr(>F)
## talkergroup_final 0.4751 0.492764
## Emotion 6.5739 0.010520 *
## Condition 51.8628 1.315e-12 ***
## Laterality 6.5143 0.001557 **
## calculator_gender_cve 0.1158 0.734537
## calculator_age_cve 0.7881 0.377503
## talkergroup_final:Emotion 0.4387 0.507925
## talkergroup_final:Condition 0.9488 0.330311
## Emotion:Condition 0.5578 0.455337
## talkergroup_final:Laterality 2.0903 0.124285
## Emotion:Laterality 0.4989 0.607374
## Condition:Laterality 0.6693 0.512347
## talkergroup_final:Emotion:Condition 7.9784 0.004845 **
## talkergroup_final:Emotion:Laterality 0.1174 0.889247
## talkergroup_final:Condition:Laterality 0.7455 0.474800
## Emotion:Condition:Laterality 0.1945 0.823293
## talkergroup_final:Emotion:Condition:Laterality 0.0920 0.912095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
plot_model(model1, type="pred", terms = c("Laterality")) 
plot_model(model1, type="pred", terms = c("Emotion")) 
model2 <- lmer(MeanAmp_N2P2 ~ talkergroup_final*Emotion*Condition + Laterality + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_good, REML = TRUE)
anova(model2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 3.68 3.68 1 75 0.4751
## Emotion 51.22 51.22 1 861 6.6189
## Condition 404.12 404.12 1 861 52.2178
## Laterality 175.16 87.58 2 861 11.3166
## calculator_gender_cve 0.90 0.90 1 75 0.1158
## calculator_age_cve 6.10 6.10 1 75 0.7881
## talkergroup_final:Emotion 3.42 3.42 1 861 0.4417
## talkergroup_final:Condition 7.39 7.39 1 861 0.9553
## Emotion:Condition 4.35 4.35 1 861 0.5617
## talkergroup_final:Emotion:Condition 62.17 62.17 1 861 8.0330
## Pr(>F)
## talkergroup_final 0.492764
## Emotion 0.010256 *
## Condition 1.095e-12 ***
## Laterality 1.408e-05 ***
## calculator_gender_cve 0.734537
## calculator_age_cve 0.377503
## talkergroup_final:Emotion 0.506474
## talkergroup_final:Condition 0.328658
## Emotion:Condition 0.453796
## talkergroup_final:Emotion:Condition 0.004701 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# MIDDLE (NO MAIN EFFECT OF EMOTION)
model1 <- lmer(MeanAmp_N2P2 ~ (talkergroup_final*Emotion*Condition) + calculator_gender_cve + calculator_age_cve + (1+Emotion+Condition|Subject), data = ZOO_FCz_Pz, REML = TRUE)
anova(model1) ## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 3.452 3.452 1 74.991 0.5977
## Emotion 10.419 10.419 1 76.998 1.8038
## Condition 106.647 106.647 1 77.000 18.4638
## calculator_gender_cve 1.085 1.085 1 75.000 0.1878
## calculator_age_cve 1.755 1.755 1 75.000 0.3039
## talkergroup_final:Emotion 0.096 0.096 1 76.998 0.0166
## talkergroup_final:Condition 0.645 0.645 1 77.000 0.1117
## Emotion:Condition 1.467 1.467 1 76.998 0.2539
## talkergroup_final:Emotion:Condition 30.449 30.449 1 76.998 5.2717
## Pr(>F)
## talkergroup_final 0.4419
## Emotion 0.1832
## Condition 5.007e-05 ***
## calculator_gender_cve 0.6660
## calculator_age_cve 0.5831
## talkergroup_final:Emotion 0.8978
## talkergroup_final:Condition 0.7392
## Emotion:Condition 0.6158
## talkergroup_final:Emotion:Condition 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model1 <- lmer(MeanAmp_N2P2 ~ talkergroup_final*Emotion*Condition + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_FCz_Pz, REML = TRUE) #by including `(1|Subject)` in your model, you are taking into account the repeated measures nature of the `Condition` and `Emotion` variables.
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 4.432 4.432 1 75 0.6230
## Emotion 12.107 12.107 1 231 1.7018
## Condition 163.494 163.494 1 231 22.9810
## calculator_gender_cve 0.471 0.471 1 75 0.0662
## calculator_age_cve 0.673 0.673 1 75 0.0946
## talkergroup_final:Emotion 0.111 0.111 1 231 0.0157
## talkergroup_final:Condition 0.989 0.989 1 231 0.1390
## Emotion:Condition 1.467 1.467 1 231 0.2061
## talkergroup_final:Emotion:Condition 30.449 30.449 1 231 4.2800
## Pr(>F)
## talkergroup_final 0.43241
## Emotion 0.19335
## Condition 2.931e-06 ***
## calculator_gender_cve 0.79767
## calculator_age_cve 0.75922
## talkergroup_final:Emotion 0.90052
## talkergroup_final:Condition 0.70963
## Emotion:Condition 0.65024
## talkergroup_final:Emotion:Condition 0.03968 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
# RIGHT SIDE: (MAIN EFFECT OF EMOTION)
ZOO_good_rightside <- subset(ZOO_good, Laterality = 'Right')
model1 <- lmer(MeanAmp_N2P2 ~ talkergroup_final*Emotion*Condition + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_good_rightside, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 3.76 3.76 1 75 0.4751
## Emotion 51.22 51.22 1 863 6.4644
## Condition 404.12 404.12 1 863 50.9985
## calculator_gender_cve 0.92 0.92 1 75 0.1158
## calculator_age_cve 6.25 6.25 1 75 0.7881
## talkergroup_final:Emotion 3.42 3.42 1 863 0.4314
## talkergroup_final:Condition 7.39 7.39 1 863 0.9330
## Emotion:Condition 4.35 4.35 1 863 0.5485
## talkergroup_final:Emotion:Condition 62.17 62.17 1 863 7.8454
## Pr(>F)
## talkergroup_final 0.492764
## Emotion 0.011179 *
## Condition 1.964e-12 ***
## calculator_gender_cve 0.734537
## calculator_age_cve 0.377503
## talkergroup_final:Emotion 0.511478
## talkergroup_final:Condition 0.334367
## Emotion:Condition 0.459113
## talkergroup_final:Emotion:Condition 0.005209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
plot_model(model1, type="pred", terms = c("Emotion")) 
# LEFT SIDE: (MAIN EFFECT OF EMOTION)
ZOO_good_leftside <- subset(ZOO_good, Laterality = 'Left')
model1 <- lmer(MeanAmp_N2P2 ~ talkergroup_final*Emotion*Condition + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_good_leftside, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 3.76 3.76 1 75 0.4751
## Emotion 51.22 51.22 1 863 6.4644
## Condition 404.12 404.12 1 863 50.9985
## calculator_gender_cve 0.92 0.92 1 75 0.1158
## calculator_age_cve 6.25 6.25 1 75 0.7881
## talkergroup_final:Emotion 3.42 3.42 1 863 0.4314
## talkergroup_final:Condition 7.39 7.39 1 863 0.9330
## Emotion:Condition 4.35 4.35 1 863 0.5485
## talkergroup_final:Emotion:Condition 62.17 62.17 1 863 7.8454
## Pr(>F)
## talkergroup_final 0.492764
## Emotion 0.011179 *
## Condition 1.964e-12 ***
## calculator_gender_cve 0.734537
## calculator_age_cve 0.377503
## talkergroup_final:Emotion 0.511478
## talkergroup_final:Condition 0.334367
## Emotion:Condition 0.459113
## talkergroup_final:Emotion:Condition 0.005209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final"))
plot_model(model1, type="pred", terms = c("Emotion"))
emmeans_resulttalkergroupCondEmo <- emmeans(model1, pairwise ~ talkergroup_final|Condition|Emotion)
emmeans_resulttalkergroupCondEmo## $emmeans
## Condition = Go, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 -6.16 0.489 116 -7.13 -5.19
## 1 -5.93 0.524 115 -6.97 -4.90
##
## Condition = NoGo, Emotion = Neg:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 -7.77 0.489 116 -8.73 -6.80
## 1 -6.87 0.524 115 -7.91 -5.83
##
## Condition = Go, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 -5.34 0.489 116 -6.31 -4.37
## 1 -4.33 0.524 115 -5.36 -3.29
##
## Condition = NoGo, Emotion = Neut:
## talkergroup_final emmean SE df lower.CL upper.CL
## 0 -7.31 0.489 116 -8.28 -6.34
## 1 -7.68 0.524 115 -8.72 -6.64
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.225 0.715 116 -0.315 0.7530
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.898 0.715 116 -1.257 0.2114
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.011 0.715 116 -1.415 0.1597
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.369 0.715 116 0.517 0.6063
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-rogerpairs(emmeans_resulttalkergroupCondEmo, adjust = "none", simple = "each") # ,adjust = "bonferroni")## $emmeans
## $`simple contrasts for talkergroup_final`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.225 0.715 116 -0.315 0.7530
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.898 0.715 116 -1.257 0.2114
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.011 0.715 116 -1.415 0.1597
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.369 0.715 116 0.517 0.6063
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## talkergroup_final = 0, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 1.605 0.355 863 4.526 <.0001
##
## talkergroup_final = 1, Emotion = Neg:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 0.933 0.378 863 2.468 0.0138
##
## talkergroup_final = 0, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 1.977 0.355 863 5.573 <.0001
##
## talkergroup_final = 1, Emotion = Neut:
## contrast estimate SE df t.ratio p.value
## Go - NoGo 3.357 0.378 863 8.884 <.0001
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## talkergroup_final = 0, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.823 0.355 863 -2.322 0.0205
##
## talkergroup_final = 1, Condition = Go:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -1.609 0.378 863 -4.258 <.0001
##
## talkergroup_final = 0, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut -0.452 0.355 863 -1.274 0.2030
##
## talkergroup_final = 1, Condition = NoGo:
## contrast estimate SE df t.ratio p.value
## Neg - Neut 0.815 0.378 863 2.157 0.0312
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
##
## $contrasts
## $`simple contrasts for contrast`
## Condition = Go, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neg:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = Go, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Condition = NoGo, Emotion = Neut:
## contrast estimate SE df z.ratio p.value
## (nothing) nonEst NA NA NA NA
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Condition`
## contrast = 0 - 1, Emotion = Neg:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo 0.672 0.518 863 1.298 0.1948
##
## contrast = 0 - 1, Emotion = Neut:
## contrast1 estimate SE df t.ratio p.value
## Go - NoGo -1.380 0.518 863 -2.664 0.0079
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger
##
## $`simple contrasts for Emotion`
## contrast = 0 - 1, Condition = Go:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut 0.786 0.518 863 1.516 0.1298
##
## contrast = 0 - 1, Condition = NoGo:
## contrast1 estimate SE df t.ratio p.value
## Neg - Neut -1.267 0.518 863 -2.445 0.0147
##
## Results are averaged over the levels of: calculator_gender_cve
## Degrees-of-freedom method: kenward-roger# Calculate marginal means for the three-way interaction
means <- as.data.frame(emmeans(model1, specs = ~ talkergroup_final:Emotion:Condition))
# Rename the levels of the "talkergroup_final" and "Emotion" factors
means$talkergroup_final <- factor(means$talkergroup_final, levels = c(0, 1), labels = c("CWNS", "CWS"))
means$Emotion <- factor(means$Emotion, levels = c("Neg", "Neut"), labels = c("Affective", "Neutral"))
bar_plot <- ggplot(means, aes(x = Emotion, y = emmean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2, position = position_dodge(0.9)) +
labs(x = "Emotion", y = "Mean N2-P2 in μV") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered"))
print(bar_plot)
# Calculate AIC for model1
aic_model1 <- AIC(model1)
# Calculate AIC for model2
aic_model2 <- AIC(model2)
# Compare AIC values
if (aic_model1 < aic_model2) {
cat("Model 1 is preferred (lower AIC)\n")
} else if (aic_model2 < aic_model1) {
cat("Model 2 is preferred (lower AIC)\n")
} else {
cat("Both models have similar AIC\n")
}## Model 2 is preferred (lower AIC)In the context of a Go/NoGo task, P3 is often associated with cognitive processing involved in decision making and response execution or inhibition. More specifically:
The P3 (P300) component is often associated with cognitive processes such as attention, decision making, and response execution or inhibition. In a Go/NoGo task, the NoGo trials require more cognitive resources as the participant must inhibit a prepotent response. This cognitive demand often results in a larger P3 component for NoGo trials.
However, it’s important to note that the specifics can vary depending on the individual and the exact design of the task. For instance, if the NoGo trials are very infrequent, the P3 amplitude might also be influenced by the novelty or surprise of these trials.
# LATERALITY - LATERALITY TALKERGROUP INTERACTION
model1 <- lmer(MeanAmp_P3 ~ talkergroup_final*Emotion*Condition*Laterality + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_good, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF
## talkergroup_final 3.69 3.69 1 75
## Emotion 9.92 9.92 1 847
## Condition 75.97 75.97 1 847
## Laterality 1932.65 966.33 2 847
## calculator_gender_cve 3.36 3.36 1 75
## calculator_age_cve 1.57 1.57 1 75
## talkergroup_final:Emotion 11.64 11.64 1 847
## talkergroup_final:Condition 2.34 2.34 1 847
## Emotion:Condition 1.42 1.42 1 847
## talkergroup_final:Laterality 103.33 51.66 2 847
## Emotion:Laterality 10.48 5.24 2 847
## Condition:Laterality 71.01 35.50 2 847
## talkergroup_final:Emotion:Condition 29.53 29.53 1 847
## talkergroup_final:Emotion:Laterality 1.96 0.98 2 847
## talkergroup_final:Condition:Laterality 56.14 28.07 2 847
## Emotion:Condition:Laterality 0.15 0.08 2 847
## talkergroup_final:Emotion:Condition:Laterality 4.78 2.39 2 847
## F value Pr(>F)
## talkergroup_final 0.2325 0.63107
## Emotion 0.6258 0.42913
## Condition 4.7923 0.02886 *
## Laterality 60.9583 < 2e-16 ***
## calculator_gender_cve 0.2122 0.64637
## calculator_age_cve 0.0988 0.75412
## talkergroup_final:Emotion 0.7346 0.39164
## talkergroup_final:Condition 0.1475 0.70101
## Emotion:Condition 0.0894 0.76504
## talkergroup_final:Laterality 3.2591 0.03890 *
## Emotion:Laterality 0.3305 0.71864
## Condition:Laterality 2.2396 0.10713
## talkergroup_final:Emotion:Condition 1.8626 0.17269
## talkergroup_final:Emotion:Laterality 0.0619 0.93994
## talkergroup_final:Condition:Laterality 1.7709 0.17082
## Emotion:Condition:Laterality 0.0047 0.99527
## talkergroup_final:Emotion:Condition:Laterality 0.1509 0.85998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Laterality", "talkergroup_final")) 
plot_model(model1, type="pred", terms = c("Condition"))
plot_model(model1, type="pred", terms = c("Laterality"))
# plot_model(model1, type="pred", terms = c("Laterality", "Condition", "talkergroup_final"))
# MIDDLE - CONDITION ONLY
model1 <- lmer(MeanAmp_P3 ~ talkergroup_final*Emotion*Condition + calculator_gender_cve + calculator_age_cve +
(1|Subject), data = ZOO_FCz_Pz, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 24.019 24.019 1 75 2.0383
## Emotion 1.483 1.483 1 231 0.1258
## Condition 131.826 131.826 1 231 11.1872
## calculator_gender_cve 0.183 0.183 1 75 0.0155
## calculator_age_cve 2.443 2.443 1 75 0.2073
## talkergroup_final:Emotion 4.736 4.736 1 231 0.4019
## talkergroup_final:Condition 0.187 0.187 1 231 0.0158
## Emotion:Condition 0.155 0.155 1 231 0.0131
## talkergroup_final:Emotion:Condition 1.912 1.912 1 231 0.1623
## Pr(>F)
## talkergroup_final 0.157531
## Emotion 0.723101
## Condition 0.000961 ***
## calculator_gender_cve 0.901109
## calculator_age_cve 0.650169
## talkergroup_final:Emotion 0.526745
## talkergroup_final:Condition 0.899958
## Emotion:Condition 0.908861
## talkergroup_final:Emotion:Condition 0.687446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_model(model1, type="pred", terms = c("Emotion" , "Condition", "talkergroup_final")) 
# Calculate marginal means for NONEXISTENT the three-way interaction
means <- as.data.frame(emmeans(model1, specs = ~ talkergroup_final:Emotion:Condition))
# Rename the levels of the "talkergroup_final" factor
means$talkergroup_final <- factor(means$talkergroup_final, levels = c(0, 1), labels = c("CWNS", "CWS"))
means$Emotion <- factor(means$Emotion, levels = c("Neg", "Neut"), labels = c("Affective", "Neutral"))
bar_plot <- ggplot(means, aes(x = Emotion, y = emmean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2, position = position_dodge(0.9)) +
labs(x = "Emotion", y = "Mean P3 in μV") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered", "royalblue2", "orangered"))
bar_plot <- bar_plot + coord_cartesian(ylim = c(4, 12))
print(bar_plot)
bar_plot <- ggplot(means, aes(x = Condition, y = emmean, fill = Emotion)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2, position = position_dodge(0.9)) +
labs(x = "Condition", y = "Mean P3 in μV") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("springgreen3", "royalblue2", "springgreen3", "royalblue2", "springgreen3", "royalblue2", "springgreen3", "royalblue2"))
bar_plot <- bar_plot + coord_cartesian(ylim = c(4, 12))
print(bar_plot)
# GO CONDITION ONLY!
ZOO_FCz_Pz_GO <- subset(ZOO_FCz_Pz, Condition == "Go")
model1 <- aov(MeanAmp_P3 ~ talkergroup_final*Emotion + calculator_gender_cve + calculator_age_cve, data = ZOO_FCz_Pz_GO)
summary(model1)## Df Sum Sq Mean Sq F value Pr(>F)
## talkergroup_final 1 53.8 53.80 2.749 0.0994 .
## Emotion 1 15.4 15.40 0.787 0.3764
## calculator_gender_cve 1 7.1 7.10 0.363 0.5478
## calculator_age_cve 1 3.1 3.10 0.158 0.6914
## talkergroup_final:Emotion 1 6.3 6.33 0.324 0.5703
## Residuals 152 2974.2 19.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model1 <- lmer(MeanAmp_P3 ~ talkergroup_final*Emotion + calculator_age_cve + calculator_gender_cve +
(1|Subject), data = ZOO_FCz_Pz_GO, REML = TRUE)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## talkergroup_final 9.2019 9.2019 1 75 1.6603 0.2015
## Emotion 1.2979 1.2979 1 77 0.2342 0.6298
## calculator_age_cve 0.5051 0.5051 1 75 0.0911 0.7636
## calculator_gender_cve 1.1453 1.1453 1 75 0.2067 0.6507
## talkergroup_final:Emotion 6.3331 6.3331 1 77 1.1427 0.2884plot_model(model1, type="pred", terms = c("Emotion"))
plot_model(model1, type="pred", terms = c("Emotion", "talkergroup_final"))
# Calculate marginal means for the three-way interaction
means <- as.data.frame(emmeans(model1, specs = ~ talkergroup_final:Emotion))
# filtered_means <- means[means$Condition == "Go", ] # keep all columns
means$talkergroup_final <- factor(means$talkergroup_final, levels = c(0, 1), labels = c("CWNS", "CWS"))
means$Emotion <- factor(means$Emotion, levels = c("Neg", "Neut"), labels = c("Affective", "Neutral"))
bar_plot <- ggplot(means, aes(x = Emotion, y = emmean)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2, position = position_dodge(0.9)) +
labs(x = "Emotion", y = "Mean P3 in μV") +
facet_grid(. ~ talkergroup_final) +
theme_minimal() +
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
legend.title =element_blank(),
legend.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold", vjust = 2),
strip.text = element_text(size = 14, face = "bold")) +
scale_fill_manual(values = c("springgreen3", "royalblue2", "springgreen3", "royalblue2"))
print(bar_plot)
PREDICTING ACCURACY USING ERP MEASURES:
collapsed_accuracy <-
ZOO_FCz_Pz %>%
group_by(Subject, talkergroup_final, Condition) %>%
summarise(
accuracy = mean(accuracy, na.rm = TRUE),
calculator_age_cve = mean(calculator_age_cve, na.rm = TRUE),
calculator_gender_cve = mean(calculator_gender_cve, na.rm = TRUE),
MeanAmp_N2P2 = mean(MeanAmp_N2P2, na.rm = TRUE),
MeanAmp_P3 = mean(MeanAmp_P3, na.rm = TRUE),
MeanAmp_P2 = mean(MeanAmp_P2, na.rm = TRUE),
Latency_N2 = mean(Latency_N2, na.rm = TRUE),
Latency_P3 = mean(Latency_P3, na.rm = TRUE),
Latency_P2 = mean(Latency_P2, na.rm = TRUE)
)## `summarise()` has grouped output by 'Subject', 'talkergroup_final'. You can
## override using the `.groups` argument.model1 <- lmer(accuracy ~ talkergroup_final*Condition + MeanAmp_N2P2 + MeanAmp_P3 +MeanAmp_P2 + Latency_N2 + Latency_P3 + Latency_P2 + calculator_age_cve+calculator_gender_cve + (1|Subject),
data = collapsed_accuracy)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## talkergroup_final 241.4 241.4 1 71.036 2.9993 0.087639 .
## Condition 3442.1 3442.1 1 103.252 42.7630 2.407e-09 ***
## MeanAmp_N2P2 51.8 51.8 1 116.361 0.6433 0.424168
## MeanAmp_P3 278.3 278.3 1 112.543 3.4575 0.065577 .
## MeanAmp_P2 409.3 409.3 1 124.183 5.0853 0.025880 *
## Latency_N2 205.6 205.6 1 144.934 2.5548 0.112138
## Latency_P3 303.7 303.7 1 141.533 3.7736 0.054053 .
## Latency_P2 0.5 0.5 1 144.761 0.0056 0.940238
## calculator_age_cve 627.4 627.4 1 75.421 7.7945 0.006636 **
## calculator_gender_cve 63.3 63.3 1 70.003 0.7862 0.378285
## talkergroup_final:Condition 76.0 76.0 1 73.489 0.9445 0.334300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AIC(model1)## [1] 1199.174model2 <- aov(accuracy ~ talkergroup_final*Condition + MeanAmp_N2P2 + MeanAmp_P3 +MeanAmp_P2 + Latency_N2 + Latency_P3 + Latency_P2 + calculator_age_cve+calculator_gender_cve +
Error(Subject/Condition), data = collapsed_accuracy)
summary(model2)##
## Error: Subject
## Df Sum Sq Mean Sq F value Pr(>F)
## talkergroup_final 1 331 330.6 2.776 0.1002
## MeanAmp_N2P2 1 9 8.9 0.075 0.7853
## MeanAmp_P3 1 739 738.7 6.202 0.0152 *
## MeanAmp_P2 1 18 18.1 0.152 0.6977
## Latency_N2 1 66 66.0 0.554 0.4591
## Latency_P3 1 222 222.2 1.865 0.1765
## Latency_P2 1 137 137.0 1.150 0.2873
## calculator_age_cve 1 631 631.3 5.300 0.0243 *
## calculator_gender_cve 1 107 107.2 0.900 0.3461
## Residuals 69 8219 119.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Subject:Condition
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 1 10847 10847 141.607 <2e-16 ***
## MeanAmp_N2P2 1 57 57 0.738 0.3932
## MeanAmp_P3 1 27 27 0.350 0.5558
## MeanAmp_P2 1 485 485 6.336 0.0141 *
## Latency_N2 1 291 291 3.797 0.0553 .
## Latency_P3 1 502 502 6.558 0.0126 *
## Latency_P2 1 70 70 0.908 0.3439
## talkergroup_final:Condition 1 109 109 1.422 0.2370
## Residuals 71 5439 77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Check the residuals of your chosen model to ensure they are normally distributed and homoscedastic. If they are not, you may need to consider transformations of your dependent variable or a different type of model.
model1 <- lmer(accuracy ~ talkergroup_final*Condition*MeanAmp_P3 + calculator_age_cve+calculator_gender_cve + (1|Subject),
data = collapsed_accuracy)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 30.09 30.09 1 111.951 0.3516
## Condition 2337.48 2337.48 1 91.214 27.3105
## MeanAmp_P3 539.47 539.47 1 108.326 6.3030
## calculator_age_cve 469.80 469.80 1 73.479 5.4890
## calculator_gender_cve 80.19 80.19 1 73.491 0.9370
## talkergroup_final:Condition 41.41 41.41 1 97.598 0.4838
## talkergroup_final:MeanAmp_P3 122.86 122.86 1 121.551 1.4355
## Condition:MeanAmp_P3 369.94 369.94 1 94.038 4.3222
## talkergroup_final:Condition:MeanAmp_P3 59.91 59.91 1 102.836 0.7000
## Pr(>F)
## talkergroup_final 0.55442
## Condition 1.09e-06 ***
## MeanAmp_P3 0.01353 *
## calculator_age_cve 0.02185 *
## calculator_gender_cve 0.33623
## talkergroup_final:Condition 0.48835
## talkergroup_final:MeanAmp_P3 0.23320
## Condition:MeanAmp_P3 0.04034 *
## talkergroup_final:Condition:MeanAmp_P3 0.40473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1MODERATOR ANALYSIS WITH ALL CORTICAL MEASURES:
#MODERATOR ANALYSIS WITH ALL CORTICAL MEASURES:
# Select columns from df1
selected_df1 <- select(ZOO_FCz_Pz, Subject, talkergroup_final, accuracy, calculator_age_cve, Condition, Emotion, MeanAmp_N2P2, MeanAmp_N2, MeanAmp_P2, MeanAmp_P3, Latency_N2, Latency_P2, Latency_P3)
# Converting into numerical variable:
encoded_data1 <- model.matrix(~ Condition + 0, data = selected_df1)
encoded_data2 <- model.matrix(~ Emotion + 0, data = selected_df1)
# Combine the original dataframe with the encoded and scaled variables
merged_df1 <- cbind(selected_df1, encoded_data1, encoded_data2)
merged_df2 <- subset(merged_df1, select = -c(Subject, ConditionNoGo, EmotionNeut, Condition, Emotion))
# Scaling the variables
# Specify the columns you want to scale
columns_to_scale <- c("MeanAmp_N2P2", "MeanAmp_N2", "MeanAmp_P2","MeanAmp_P3", "Latency_N2","Latency_P2", "Latency_P3")
# Subset the data frame to include only the columns you want to scale
subset_df <- merged_df2[, columns_to_scale]
# Scale the selected columns
scaled_subset_df <- as.data.frame(scale(subset_df))
# subjects <- subset(merged_df1, select = c(Subject))
# Get the Unscaled columns + Subjects Column
merged_df1_small <- subset(merged_df1, select = c( Subject, talkergroup_final, accuracy, calculator_age_cve, ConditionGo, EmotionNeg))
# Combine the scaled subset with the original data frame
final_merged <- cbind(merged_df1_small, scaled_subset_df)
# Rename condition and emotion
final_merged <- final_merged %>%
rename(Condition = ConditionGo, Emotion = EmotionNeg)
model1 <- lmer(accuracy ~ talkergroup_final*Condition*Emotion + MeanAmp_N2P2 + MeanAmp_P3 +MeanAmp_P2 + Latency_N2 + Latency_P3 + Latency_P2 + calculator_age_cve + (1|Subject),
data = final_merged)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## talkergroup_final 378.7 378.7 1 248.567 3.9877
## Condition 4198.9 4198.9 1 249.518 44.2122
## Emotion 0.1 0.1 1 228.887 0.0008
## MeanAmp_N2P2 46.2 46.2 1 254.889 0.4861
## MeanAmp_P3 370.9 370.9 1 251.185 3.9052
## MeanAmp_P2 412.2 412.2 1 271.839 4.3402
## Latency_N2 101.9 101.9 1 298.685 1.0730
## Latency_P3 237.9 237.9 1 300.334 2.5047
## Latency_P2 3.0 3.0 1 299.501 0.0318
## calculator_age_cve 714.4 714.4 1 81.859 7.5220
## talkergroup_final:Condition 151.2 151.2 1 230.361 1.5922
## talkergroup_final:Emotion 35.2 35.2 1 230.058 0.3709
## Condition:Emotion 22.9 22.9 1 228.521 0.2416
## talkergroup_final:Condition:Emotion 41.3 41.3 1 232.415 0.4349
## Pr(>F)
## talkergroup_final 0.046925 *
## Condition 1.846e-10 ***
## Emotion 0.977713
## MeanAmp_N2P2 0.486306
## MeanAmp_P3 0.049231 *
## MeanAmp_P2 0.038157 *
## Latency_N2 0.301115
## Latency_P3 0.114561
## Latency_P2 0.858656
## calculator_age_cve 0.007485 **
## talkergroup_final:Condition 0.208295
## talkergroup_final:Emotion 0.543140
## Condition:Emotion 0.623498
## talkergroup_final:Condition:Emotion 0.510256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1AIC(model1)## [1] 2397.326model1 <- lmer(accuracy ~ talkergroup_final*Condition*Emotion*MeanAmp_P3 + calculator_age_cve + (1|Subject),
data = final_merged)
anova(model1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF
## talkergroup_final 408.6 408.6 1 244.957
## Condition 4223.6 4223.6 1 228.392
## Emotion 60.4 60.4 1 225.257
## MeanAmp_P3 4.8 4.8 1 289.508
## calculator_age_cve 557.4 557.4 1 74.805
## talkergroup_final:Condition 209.6 209.6 1 231.550
## talkergroup_final:Emotion 179.5 179.5 1 224.332
## Condition:Emotion 148.3 148.3 1 224.166
## talkergroup_final:MeanAmp_P3 8.4 8.4 1 282.984
## Condition:MeanAmp_P3 5.4 5.4 1 253.784
## Emotion:MeanAmp_P3 737.2 737.2 1 248.298
## talkergroup_final:Condition:Emotion 193.3 193.3 1 224.871
## talkergroup_final:Condition:MeanAmp_P3 3.9 3.9 1 257.942
## talkergroup_final:Emotion:MeanAmp_P3 410.1 410.1 1 254.016
## Condition:Emotion:MeanAmp_P3 249.5 249.5 1 247.997
## talkergroup_final:Condition:Emotion:MeanAmp_P3 76.1 76.1 1 249.498
## F value Pr(>F)
## talkergroup_final 4.3969 0.037030 *
## Condition 45.4472 1.267e-10 ***
## Emotion 0.6501 0.420933
## MeanAmp_P3 0.0513 0.821019
## calculator_age_cve 5.9983 0.016661 *
## talkergroup_final:Condition 2.2556 0.134497
## talkergroup_final:Emotion 1.9312 0.166002
## Condition:Emotion 1.5954 0.207870
## talkergroup_final:MeanAmp_P3 0.0901 0.764208
## Condition:MeanAmp_P3 0.0580 0.809844
## Emotion:MeanAmp_P3 7.9321 0.005247 **
## talkergroup_final:Condition:Emotion 2.0797 0.150658
## talkergroup_final:Condition:MeanAmp_P3 0.0416 0.838564
## talkergroup_final:Emotion:MeanAmp_P3 4.4131 0.036651 *
## Condition:Emotion:MeanAmp_P3 2.6844 0.102605
## talkergroup_final:Condition:Emotion:MeanAmp_P3 0.8190 0.366337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot1 <- plot_model(model1, type='pred', terms = c("MeanAmp_P3", "Emotion", "talkergroup_final")) + aes(linetype = group, color = group) +
theme_bw() +
labs(x = "Mean P3 Amplitude (Scaled)", y = "Accuracy (%)", fill = "talkergroup_final")+
theme(axis.title.x = element_text(family="Arial", color = "grey20", size = 14, angle = 0),
axis.title.y = element_text(family="Arial", color = "grey20", size = 14, angle = 90),
axis.text.x = element_text(family="Arial", color = "grey20", size = 12, angle = 0),
axis.text.y = element_text(family="Arial", color = "grey20", size = 12, angle = 0),
legend.text = element_text(family="Arial", color = "grey20", size = 12, angle = 0),
title = element_blank(),
legend.title = element_blank())+
scale_color_manual(values = c("mediumpurple1", "springgreen3"), labels = c("Neutral", "Affective")) +
scale_fill_manual(values = c("mediumpurple1", "springgreen3"), labels = c("Neutral", "Affective")) ## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(plot1)
#FIT MULTIPLE LINEAR MODELS FOR EACH TALKERGROUP AND EMOTION
# Create an empty data frame to store the results
results_df <- data.frame(
Emotion = character(),
TalkerGroup = character(),
P_Value = numeric(),
stringsAsFactors = FALSE
)
# Create a subset of your data for each combination of emotion and talkergroup
subsets <- final_merged %>%
filter(Emotion %in% c(0,1), talkergroup_final %in% c(0, 1))
# Loop through each combination and extract the p-values
for (emotion in unique(subsets$Emotion)) {
for (group in unique(subsets$talkergroup_final)) {
subset_data <- subsets %>%
filter(Emotion == emotion, talkergroup_final == group)
# Fit a linear regression model for the current combination
model <- lm(accuracy ~ MeanAmp_P3, data = subset_data)
# Extract the p-value
p_value <- summary(model)$coefficients[2, 4] # Assumes MeanAmp_P3 is the second coefficient
# Create a data frame with the results
result_row <- data.frame(
Emotion = emotion,
TalkerGroup = paste("talkergroup", group),
P_Value = p_value
)
# Append the results to the results data frame
results_df <- rbind(results_df, result_row)
}
}
# Print the results
print(results_df)## Emotion TalkerGroup P_Value
## 1 1 talkergroup 1 0.52452700
## 2 1 talkergroup 0 0.06758877
## 3 0 talkergroup 1 0.58951614
## 4 0 talkergroup 0 0.78517833CORRELATIONS BETWEEN ERPs and ACCURACY
ZOO_Corr_NoGo_Neg_CWS <- subset(ZOO_FCz_Pz, Condition == "NoGo" & Emotion == "Neg" & talkergroup_final == 1)
ZOO_Corr_NoGo_Neut_CWS <- subset(ZOO_FCz_Pz, Condition == "NoGo" & Emotion == "Neut" & talkergroup_final == 1)
ZOO_Corr_Go_Neg_CWS <- subset(ZOO_FCz_Pz, Condition == "Go" & Emotion == "Neg" & talkergroup_final == 1)
ZOO_Corr_Go_Neut_CWS <- subset(ZOO_FCz_Pz, Condition == "Go" & Emotion == "Neut" & talkergroup_final == 1)
ZOO_Corr_NoGo_Neg_CWNS <- subset(ZOO_FCz_Pz, Condition == "NoGo" & Emotion == "Neg" & talkergroup_final == 0)
ZOO_Corr_NoGo_Neut_CWNS <- subset(ZOO_FCz_Pz, Condition == "NoGo" & Emotion == "Neut" & talkergroup_final == 0)
ZOO_Corr_Go_Neg_CWNS <- subset(ZOO_FCz_Pz, Condition == "Go" & Emotion == "Neg" & talkergroup_final == 0)
ZOO_Corr_Go_Neut_CWNS <- subset(ZOO_FCz_Pz, Condition == "Go" & Emotion == "Neut" & talkergroup_final == 0)
rownames(ZOO_Corr_NoGo_Neg_CWS) <- NULL
rownames(ZOO_Corr_NoGo_Neut_CWS) <- NULL
rownames(ZOO_Corr_Go_Neg_CWS) <- NULL
rownames(ZOO_Corr_Go_Neut_CWS) <- NULL
rownames(ZOO_Corr_NoGo_Neg_CWNS) <- NULL
rownames(ZOO_Corr_NoGo_Neut_CWNS) <- NULL
rownames(ZOO_Corr_Go_Neg_CWNS) <- NULL
rownames(ZOO_Corr_Go_Neut_CWNS) <- NULL
#DRAW CORRELATION PLOTS###########################
correlation_analysis <- function(df1, df2, var1, var2) {
# Perform the correlation test
correlation_CWS <- cor.test(df1[[var1]], df1[[var2]], use="complete.obs") #exclude pairs with missing values
correlation_CWNS <- cor.test(df2[[var1]], df2[[var2]], use="complete.obs")
# Create a scatter plot with a regression line and confidence interval
# use `aes_string()` or aes with !!sym when you're passing variable names as strings.
title <- paste(deparse(substitute(df1)), "and", deparse(substitute(df2)))
ggplot() +
geom_point(data = df1, aes(x = !!sym(var1), y = !!sym(var2)), color = "red") +
geom_smooth(data = df1, aes(x = !!sym(var1), y = !!sym(var2)), method = "lm", se = TRUE, col = "red") +
geom_point(data = df2, aes(x = !!sym(var1), y = !!sym(var2)), color = "blue") +
geom_smooth(data = df2, aes(x = !!sym(var1), y = !!sym(var2)), method = "lm", se = TRUE, col = "blue") +
labs(title = title, x = "ERP Amplitude (µV)", y = "Accuracy (%)") +
theme_minimal() + # Set background to white
theme(panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
axis.line = element_line(color = "black"),
axis.text = element_text(size = 14), # Increase font size for both axis labels and tick labels
axis.title = element_text(size = 16)) + # Increase font size for axis labels
ylim(40, 110) + # Replace ymin_value and ymax_value with your desired limits
annotate("text", x = mean(df1[[var1]]), y = max(df1[[var2]]) + 5,
label = paste("CWS r =", round(correlation_CWS$estimate, 3)), vjust = -1, col = "red") +
annotate("text", x = mean(df1[[var1]]), y = max(df1[[var2]]),
label = paste("p-value =", round(correlation_CWS$p.value, 3)), vjust = -1, col = "red") +
annotate("text", x = mean(df2[[var1]]), y = min(df2[[var2]]) + 5,
label = paste("CWNS r =", round(correlation_CWNS$estimate, 3)), vjust = -1, col = "blue") +
annotate("text", x = mean(df2[[var1]]), y = min(df2[[var2]]),
label = paste("p-value =", round(correlation_CWNS$p.value, 3)), vjust = -1, col = "blue") +
theme(legend.position = "none")
}
correlation_analysis(ZOO_Corr_NoGo_Neg_CWS, ZOO_Corr_NoGo_Neg_CWNS, "MeanAmp_N2P2", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_NoGo_Neut_CWS, ZOO_Corr_NoGo_Neut_CWNS, "MeanAmp_N2P2", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_Go_Neg_CWS, ZOO_Corr_Go_Neg_CWNS, "MeanAmp_N2P2", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_Go_Neut_CWS, ZOO_Corr_Go_Neut_CWNS, "MeanAmp_N2P2", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_NoGo_Neg_CWS, ZOO_Corr_NoGo_Neg_CWNS, "MeanAmp_P3", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_NoGo_Neut_CWS, ZOO_Corr_NoGo_Neut_CWNS, "MeanAmp_P3", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_Go_Neg_CWS, ZOO_Corr_Go_Neg_CWNS, "MeanAmp_P3", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
correlation_analysis(ZOO_Corr_Go_Neut_CWS, ZOO_Corr_Go_Neut_CWNS, "MeanAmp_P3", "accuracy")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
#PERSONALIZED PLOT######
correlation_CWNS_Neg_GO <- cor.test(ZOO_Corr_NoGo_Neg_CWS$MeanAmp_P3, ZOO_Corr_NoGo_Neg_CWS$accuracy)
correlation_CWNS_Neg_NOGO <- cor.test(ZOO_Corr_NoGo_Neg_CWNS$MeanAmp_P3, ZOO_Corr_NoGo_Neg_CWNS$accuracy)
# Create a scatter plot with a regression line and confidence interval
ggplot() +
geom_point(data = ZOO_Corr_NoGo_Neg_CWS, aes(x = MeanAmp_P3, y = accuracy), color = "blue") +
geom_smooth(data = ZOO_Corr_NoGo_Neg_CWS, aes(x = MeanAmp_P3, y = accuracy), method = "lm", se = TRUE, col = "blue") +
geom_point(data = ZOO_Corr_NoGo_Neg_CWNS, aes(x = MeanAmp_P3, y = accuracy), color = "red") +
geom_smooth(data = ZOO_Corr_NoGo_Neg_CWNS, aes(x = MeanAmp_P3, y = accuracy), method = "lm", se = TRUE, col = "red") +
labs(title = "CWNS in Affective GO and NOGO", x = "P3 Amplitude (µV)", y = "Accuracy (%)") +
theme_minimal() + # Set background to white
theme(panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
axis.line = element_line(color = "black"),
axis.text = element_text(size = 14), # Increase font size for both axis labels and tick labels
axis.title = element_text(size = 16)) + # Increase font size for axis labels
ylim(40, 110) + # Replace ymin_value and ymax_value with your desired limits
annotate("text", x = mean(ZOO_Corr_NoGo_Neg_CWS$MeanAmp_P3), y = max(ZOO_Corr_NoGo_Neg_CWS$accuracy) + 5,
label = paste("Affective GO r =", round(correlation_CWNS_Neg_GO$estimate, 3)), vjust = -1, col = "blue") +
annotate("text", x = mean(ZOO_Corr_NoGo_Neg_CWS$MeanAmp_P3), y = max(ZOO_Corr_NoGo_Neg_CWS$accuracy),
label = paste("p-value =", round(correlation_CWNS_Neg_GO$p.value, 3)), vjust = -1, col = "blue") +
annotate("text", x = mean(ZOO_Corr_NoGo_Neg_CWNS$MeanAmp_P3), y = min(ZOO_Corr_NoGo_Neg_CWNS$accuracy) + 5,
label = paste("Affective NOGO r =", round(correlation_CWNS_Neg_NOGO$estimate, 3)), vjust = -1, col = "red") +
annotate("text", x = mean(ZOO_Corr_NoGo_Neg_CWNS$MeanAmp_P3), y = min(ZOO_Corr_NoGo_Neg_CWNS$accuracy),
label = paste("p-value =", round(correlation_CWNS_Neg_NOGO$p.value, 3)), vjust = -1, col = "red") +
theme(legend.position = "none")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'